Topological evolution of tumor imagery

ABSTRACT

Topological evolution of a lesion within a time series of medical imagery is provided. In various embodiments, a time series of medical images is read. Each of the images depicts a subject anatomy and a lesion. The lesion has a size and a contour within each of the medical images. At least one anatomical label is read for the subject anatomy within each of the plurality of images. Based upon the contour of the lesion within each of the medical images and based on the at least one anatomical label, a further contour of the lesion is predicted outside of the time series.

BACKGROUND

Embodiments of the present invention relate to medical imagery, and morespecifically, to topological evolution of a lesion within a time seriesof medical imagery.

BRIEF SUMMARY

According to various embodiments of the present disclosure, methods ofand computer program products for topological evolution of a lesion areprovided. A time series of medical images is read. Each of the imagesdepicts a subject anatomy and a lesion. The lesion has a size and acontour within each of the medical images. At least one anatomical labelis read for the subject anatomy within each of the plurality of images.Based upon the contour of the lesion within each of the medical imagesand based on the at least one anatomical label, a further contour of thelesion is predicted outside of the time series.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 depicts an exemplary Picture Archiving and Communication System.

FIG. 2 illustrates an exemplary clinical image search and retrievalmethod.

FIG. 3 illustrates a tumor propagation model according to embodiments ofthe present disclosure.

FIG. 4 illustrates exemplary propagation of a tumor in brain MRIaccording to embodiments of the present disclosure.

FIG. 5 illustrates exemplary propagation of an edema in brain MMaccording to embodiments of the present disclosure.

FIG. 6 illustrates a system for generation of candidate lesions throughlesion progression according to embodiments of the present disclosure.

FIG. 7 illustrates exemplary candidate lesion identification accordingto embodiments of the present disclosure.

FIG. 8 illustrates exemplary multiple candidate generation according toembodiments of the present disclosure.

FIG. 9 illustrates a method of topological evolution of medical imageryaccording to embodiments of the present disclosure.

FIG. 10 depicts a computing node according to an embodiment of thepresent invention.

DETAILED DESCRIPTION

Tumor tracking and progression analysis using medical images is acrucial task for physicians to provide accurate and efficient treatmentplans. The present disclosure provides a generalized tumor propagationmodel considering time-series prior images and local anatomicalfeatures. In some embodiments, a hierarchical Hidden Markov Model isused for tumor tracking. In other embodiments, a dynamic Bayesiannetwork (DBN), conditional random field (CRF), a support vector machine(SVM), or an recurrent neural network (RNN) is used. It will beappreciated that a variety of learning systems and algorithms suitablefor time series data may be applied for tumor tracking according to thepresent disclosure. The propagation model describes the lesionpropagation between prior and current images, and between adjacentimages within the same series. The anatomical tissue structure of thetargeted body part is extracted and the local tissue properties areincorporated in the model. The model identifies a diagnosticrelationship between different tissue types, their locations andneighboring regions.

The Tumor tracking models according to embodiments of the presentdisclosure are suitable for integration into various clinical systems.For example, topological evolution of contour imagery according toembodiments of the present disclosure may be used together with manualannotations or an automated CAD system. The teachings of the presentdisclosure can support the manual annotation process, leading to speedand accuracy increases. Similarly, the teachings of the presentdisclosure can compensate for the risk of inaccurate outcomes from CADsystems.

The methods set out herein are suitable for application to diverse tumorexams including brain tumors, lung nodules and liver cancer exams withpriors. The present disclosure is applicable to general tumorpropagation problems for practical use in a tumor tracking application.

A Picture Archiving and Communication System (PACS) is a medical imagingsystem that provides storage and access to images from multiplemodalities. In many heathcare environments, electronic images andreports are transmitted digitally via PACS, thus eliminating the need tomanually file, retrieve, or transport film jackets. A standard formatfor PACS image storage and transfer is DICOM (Digital Imaging andCommunications in Medicine). Non-image data, such as scanned documents,may be incorporated using various standard formats such as PDF (PortableDocument Format) encapsulated in DICOM.

The present disclosure provides a generalized lesion propagation modelto improve tumor and other feature tracking and predication analysis inmedical images. The model incorporates time-series lesion informationand local neighboring tissue information. Various embodiments takeadvantage of the observation that the probability of tumor detection ina given exam is conditionally dependent upon the presence of tumors inprior exams, and that the local tissue type surrounding the tumor has aspatial relationship with the tumor's local growth or reduction in size.The model may be incorporated together with user-guided tumorsegmentation in an oncology viewer application according to variousembodiments of the present disclosure.

Hand annotation is error prone and time consuming. Simple edge detectiondoes not take into account the progression of a mass over time. Toaddress this problem, the present disclosure provides for evolution ofidentified masses as an aid to annotation.

According to various embodiments of the present disclosure, a mass isinitially hand annotated. Treatment information is input to the system,and additional imagery is hand annotated. The hand annotated contoursare used to train an image analysis system. Once trained, the imageanalysis system is used to evolve a new contour. The new contour isoverlain on subsequently gathered imagery as a suggested contour. A userthen has the ability to adjust or accept the contour.

By deriving a projected new contour on the basis of treatmentinformation, the user need not hand annotate from scratch, therebyreducing errors. This is less computationally intensive than performingnew feature extraction.

In various embodiments, multiple tumor volumes (or contours) aregenerated with an evolutional learning process using other tumor images.Other tumor images may include tumor evolution features from the currentexam's priors or other patient's exams. The prior exam images arealigned to the current exam images utilizing neighboring anatomicalfeatures around the lesion. The evolutional learning process includestumor growth, shrinking, splitting into multiple tumors. Evolutionlearning process uses the following tumor characteristics includinggrowing or shrinking near the area of prior tumor and tumor spreadingspeed could be different based on neighboring tissue characteristics.

In various embodiments, a user may select the target tumor withdifferent scores or colors. The generated multiple tumor volumes mayhave assigned probabilities based on the relevance of similarity withthe existing priors. This may be translated into scores or colors. Therelevancy of similarity includes the intensity, texture, tumor shape,neighbor tissue, or speed of tumor growth/shrink.

Referring to FIG. 1, an exemplary PACS 100 consists of four majorcomponents. Various imaging modalities 101 . . . 109 such as computedtomography (CT) 101, magnetic resonance imaging (MM) 102, or ultrasound(US) 103 provide imagery to the system. In some implementations, imageryis transmitted to a PACS Gateway 111, before being stored in archive112. Archive 112 provides for the storage and retrieval of images andreports. Workstations 121 . . . 129 provide for interpreting andreviewing images in archive 112. In some embodiments, a secured networkis used for the transmission of patient information between thecomponents of the system. In some embodiments, workstations 121 . . .129 may be web-based viewers. PACS delivers timely and efficient accessto images, interpretations, and related data, eliminating the drawbacksof traditional film-based image retrieval, distribution, and display.

A PACS may handle images from various medical imaging instruments, suchas X-ray plain film (PF), ultrasound (US), magnetic resonance (MR),Nuclear Medicine imaging, positron emission tomography (PET), computedtomography (CT), endoscopy (ES), mammograms (MG), digital radiography(DR), computed radiography (CR), Histopathology, or ophthalmology.However, a PACS is not limited to a predetermined list of images, andsupports clinical areas beyond conventional sources of imaging such asradiology, cardiology, oncology, or gastroenterology.

Different users may have a different view into the overall PACS system.For example, while a radiologist may typically access a viewing station,a technologist may typically access a QA workstation.

In some implementations, the PACS Gateway 111 comprises a qualityassurance (QA) workstation. The QA workstation provides a checkpoint tomake sure patient demographics are correct as well as other importantattributes of a study. If the study information is correct the imagesare passed to the archive 112 for storage. The central storage device,archive 112, stores images and in some implementations, reports,measurements and other information that resides with the images.

Once images are stored to archive 112, they may be accessed from readingworkstations 121 . . . 129. The reading workstation is where aradiologist reviews the patient's study and formulates their diagnosis.In some implementations, a reporting package is tied to the readingworkstation to assist the radiologist with dictating a final report. Avariety of reporting systems may be integrated with the PACS, includingthose that rely upon traditional dictation. In some implementations, CDor DVD authoring software is included in workstations 121 . . . 129 toburn patient studies for distribution to patients or referringphysicians.

In some implementations, a PACS includes web-based interfaces forworkstations 121 . . . 129. Such web interfaces may be accessed via theinternet or a Wide Area Network (WAN). In some implementations,connection security is provided by a VPN (Virtual Private Network) orSSL (Secure Sockets Layer). The clients side software may compriseActiveX, JavaScript, or a Java Applet. PACS clients may also be fullapplications which utilize the full resources of the computer they areexecuting on outside of the web environment.

Communication within PACS is generally provided via Digital Imaging andCommunications in Medicine (DICOM). DICOM provides a standard forhandling, storing, printing, and transmitting information in medicalimaging. It includes a file format definition and a networkcommunications protocol. The communication protocol is an applicationprotocol that uses TCP/IP to communicate between systems. DICOM filescan be exchanged between two entities that are capable of receivingimage and patient data in DICOM format.

DICOM groups information into data sets. For example, a file containinga particular image, generally contains a patient ID within the file, sothat the image can never be separated from this information by mistake.A DICOM data object consists of a number of attributes, including itemssuch as name and patient ID, as well as a special attribute containingthe image pixel data. Thus, the main object has no header as such, butinstead comprises a list of attributes, including the pixel data. ADICOM object containing pixel data may correspond to a single image, ormay contain multiple frames, allowing storage of cine loops or othermulti-frame data. DICOM supports three- or four-dimensional dataencapsulated in a single DICOM object. Pixel data may be compressedusing a variety of standards, including JPEG, Lossless JPEG, JPEG 2000,and Run-length encoding (RLE). LZW (zip) compression may be used for thewhole data set or just the pixel data.

Referring to FIG. 2, an exemplary PACS image search and retrieval method200 is depicted. Communication with a PACS server, such as archive 112,is done through DICOM messages that that contain attributes tailored toeach request. At 201, a client, such as workstation 121, establishes anetwork connection to a PACS server. At 202, the client prepares a DICOMmessage, which may be a C-FIND, C-MOVE, C-GET, or C-STORE request. At203, the client fills in the DICOM message with the keys that should bematched. For example, to search by patient ID, a patient ID attribute isincluded. At 204, the client creates empty attributes for all the valuesthat are being requested from the server. For example, if the client isrequesting an image ID suitable for future retrieval of an image, itinclude an empty attribute for an image ID in the message. At 205, theclient send the message to the server. At 206, the server sends back tothe client a list of one or more response messages, each of whichincludes a list of DICOM attributes, populated with values for eachmatch.

Referring to FIG. 3, a tumor propagation model according to embodimentsof the present disclosure is illustrated. A generalized Hidden MarkovModel is applied for each tissue type to model the relationships betweenprior lesions in the same tissue type as well as the relationships ofthe current predicted lesions between the neighboring tissue types toidentify the tumors in an image series.

Given an image series 301 with 1 images, let f^(i) denote the i^(th)image in the series; then, image series f^(i) is composed of K disjointtissue regions r_(k) ^(i), k=1, . . . , K. The neighborhood ∂_(j,k) ^(i)of tissue region r_(k) ^(i) is defined as the set of tissue regionsadjacent to region r_(k) ^(i). Following the stochastic HMM framework, ahidden state s_(k) ^(i) is defined, representing whether or not thelesion is contained in tissue region r_(k) ^(i). An observation o_(k)^(i) is defined by the part of the images corresponding to tissue regionr_(k) ^(i). Finally, random variables S_(k) ^(i) 302 and O_(k) ^(i) 303are defined to represent state s_(k) ^(i), and observation o_(k) ^(i).Then, by the Markov property, the joint conditional probability densityfunction p(s|O=o) follows a Gibbs distribution, and is written accordingto Equation 1, where s={s_(k) ^(i)|i=1, . . . , I and k=1, . . . , K},O={O_(k) ^(i)|i=1, . . . , I and k=1, . . . , K}, o={o_(k) ^(i)|i=1, . .. , I and k=1, . . . , K}, N_(k) ^(i) denotes the set of states of theneighbor tissues of s_(k) ^(i), such that N_(k) ^(i)={s_(l) ^(i)|r_(l)^(i)∈∂_(k) ^(i), l=1, . . . , K}, t is a transition feature function, uis a state feature function, λ and μ are parameters to be estimated, andZ(o) is a normalization factor according to Equation 2.

$\begin{matrix}{{p\left( {{sO} = o} \right)} = {\frac{1}{Z(o)}{\exp\left( {\sum\limits_{i = 1}^{i = 1}\; \left( {{\sum\limits_{{k = 1},{\sigma^{i} \in N_{k}^{i}}}^{k = K}\; {\lambda \cdot {t\left( {\sigma^{i},s_{k}^{i - 1},s_{k}^{i},o_{k}^{i}} \right)}}} + {\sum\limits_{k = 1}^{K}\; {\mu \cdot {u\left( {s_{k}^{i},o_{k}^{i}} \right)}}}} \right)} \right)}}} & {{Equation}\mspace{14mu} 1} \\{{Z(o)} = {\sum\limits_{s}\; {\exp\left( {\sum\limits_{i = 1}^{i = 1}\; \left( {{\sum\limits_{{k = 1},{\sigma^{i} \in N_{k}^{i}}}^{k = K}\; {\lambda \cdot {t\left( {\sigma^{i},s_{k}^{i - 1},s_{k}^{i},o_{k}^{i}} \right)}}} + {\sum\limits_{k = 1}^{K}\; {\mu \cdot {u\left( {s_{k}^{i},o_{k}^{i}} \right)}}}} \right)} \right)}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$

Referring to FIGS. 4-5, exemplary propagation of a tumor and an edemaare illustrated. Each figure depicts an exemplary MM brain image. Eachfigure shows results of tumor and edema propagation between prior andcurrent brain exams. The three brain MR exams in each sequence wereacquired within a 10 month span. In FIG. 4, predicted outlines of atumor are depicted at three points in time 401 . . . 403. In FIG. 5,predicted outlines of an edema are depicted at three points in time 501. . . 503.

Referring to FIG. 6, a system for generation of candidate lesionsthrough lesion progression (e.g., growth, shrink, split) is illustratedaccording to embodiments of the present disclosure. A plurality of priorstudy images 601 . . . 602 associated with a patient record 603 arealigned according to the neighboring anatomic features. In variousembodiments, registration is performed to align images. In exemplaryimage 601, lesion 604 has neighboring tissue features 605 . . . 606.These features may be used to align images 601 . . . 602 irrespective ofthe evolution of lesion 604. In exemplary image 602, lesion 604 hasexpanded into lesion 607. In various embodiments, multiple patientrecords 603 . . . 605 may be analyzed as set forth above.

Prior images 601 . . . 602 are used to train a feature learner 608. Insome embodiments, feature learner 608 comprises a dynamic Bayesiannetwork (DBN) such as a hidden Markov model (HMM). In some embodiments,feature learner 608 comprises a conditional random field (CRF) or asupport vector machine (SVM). In some embodiments, where the priorimages are not manually labeled, various segmentation algorithms may beapplied to determine feature locations prior to learning. In variousembodiments, the feature learner may be provided various characteristicsof a lesion including intensity, lesion shape, texture, neighboringtissue characteristics, growth or shrinkage rate. For example, in theabove example, the feature learner may discern that the lesion growsfaster into tissue 606 than into tissue 605.

Referring to FIG. 7, exemplary candidate lesion identification isillustrated according to embodiments of the present disclosure. When newimage 701 is collected, it is correlated to prior images 601 . . . 602.Tissue features 605 and 606 are thereby located. Applying the trainedlearner to input image 701, a current shape of lesion 702 is predicted,as is additional candidate lesion 703. In this way, candidate lesionsfor examination or labeling may be generated from a time series ofimages for a given patient, or from a plurality of time series for morethan one patient.

Referring to FIG. 8, exemplary multiple candidate generation isillustrated according to embodiments of the present disclosure. Priorvolume 801 includes lesion 802. Based on prior image 801, candidatelesions 803 . . . 805 are identified in current volume 806.

As set forth above, according to various embodiments, a tumorpropagation model is provided using a generalized HMM incorporatingneighboring tissue and time series lesion information. This model may beincorporated with an expert-guided or automatic lesion segmentationalgorithm to propagate lesions within and between prior and currentexams.

Referring to FIG. 9, a method for topological evolution of a lesion isillustrated. At 901, a time series of medical images is read. Each ofthe images depicts a subject anatomy and a lesion. The lesion has a sizeand a contour within each of the medical images. At 902, at least oneanatomical label is read for the subject anatomy within each of theplurality of images. At 903, based upon the contour of the lesion withineach of the medical images and based on the at least one anatomicallabel, a further contour of the lesion is predicted outside of the timeseries.

Referring now to FIG. 10, a schematic of an example of a computing nodeis shown. Computing node 10 is only one example of a suitable computingnode and is not intended to suggest any limitation as to the scope ofuse or functionality of embodiments of the invention described herein.Regardless, computing node 10 is capable of being implemented and/orperforming any of the functionality set forth hereinabove.

In computing node 10 there is a computer system/server 12, which isoperational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 10, computer system/server 12 in computing node 10 isshown in the form of a general-purpose computing device. The componentsof computer system/server 12 may include, but are not limited to, one ormore processors or processing units 16, a system memory 28, and a bus 18that couples various system components including system memory 28 toprocessor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method comprising: reading a time series of medical images, each of the images depicting a subject anatomy and a lesion, the lesion having a size and a contour within each of the medical images; reading at least one anatomical label for the subject anatomy within each of the plurality of images; based upon the contour of the lesion within each of the medical images and based on the at least one anatomical label, predicting a further contour of the lesion outside of the time series.
 2. The method of claim 1, wherein predicting the further contour comprises: applying a dynamic Bayesian network, a conditional random field, a support vector machine, or a recurrent neural network to the time series of medical images.
 3. The method of claim 1, wherein predicting the further contour comprises: applying a hidden Markov model to the time series of medical images.
 4. The method of claim 1, wherein predicting the further contour comprises: determining a plurality of characteristics of the lesion over the time series.
 5. The method of claim 4, wherein the plurality of characteristics comprise: size, shape, growth rate, shrinkage rate, intensity, texture, or neighboring tissue characteristics.
 6. The method of claim 1, further comprising: presenting to a user the further contour overlain on a further medical image corresponding to a time later than the time series.
 7. The method of claim 1, further comprising: aligning the medical images based on the at least one anatomical label.
 8. The method of claim 7, wherein aligning the medical images comprises performing registration between the medical images.
 9. The method of claim 1, further comprising: predicting at least one additional lesion.
 10. The method of claim 1, wherein applying the dynamic Bayesian network comprises: dividing each of the medical images into a plurality of disjoint regions; and determining whether the lesion is contains in each of the plurality of disjoint regions.
 11. The method of claim 1, wherein applying the dynamic Bayesian network comprises: dividing each of the medical images into a plurality of disjoint regions; and determining at least one tissue characteristic for each of the plurality of disjoint regions.
 12. A system comprising: a data store comprising a time series of medical images, each of the images depicting a subject anatomy and a lesion, the lesion having a size and a contour within each of the medical images; a computing node comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor of the computing node to cause the processor to perform a method comprising: reading the time series of medical images; reading at least one anatomical label for the subject anatomy within each of the plurality of images; based upon the contour of the lesion within each of the medical images and based on the at least one anatomical label, predicting a further contour of the lesion outside of the time series.
 13. The system of claim 12, wherein predicting the further contour comprises: applying a dynamic Bayesian network to the time series of medical images.
 14. The system of claim 13, wherein the dynamic Bayesian network comprises a hidden Markov model.
 15. The system of claim 12, wherein predicting the further contour comprises: determining a plurality of characteristics of the lesion over the time series.
 16. The system of claim 15, wherein the plurality of characteristics comprise: size, shape, growth rate, shrinkage rate, intensity, texture, or neighboring tissue characteristics.
 17. The system of claim 12, further comprising: a display, and wherein the method further comprises: presenting on the display the further contour overlain on a further medical image corresponding to a time later than the time series.
 18. The method of claim 1, wherein applying the dynamic Bayesian network comprises: dividing each of the medical images into a plurality of disjoint regions; and determining whether the lesion is contains in each of the plurality of disjoint regions.
 19. The method of claim 1, wherein applying the dynamic Bayesian network comprises: dividing each of the medical images into a plurality of disjoint regions; and determining at least one tissue characteristic for each of the plurality of disjoint regions.
 20. A computer program product for topological evolution of a lesion, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform a method comprising: reading a time series of medical images, each of the images depicting a subject anatomy and a lesion, the lesion having a size and a contour within each of the medical images; reading at least one anatomical label for the subject anatomy within each of the plurality of images; based upon the contour of the lesion within each of the medical images and based on the at least one anatomical label, predicting a further contour of the lesion outside of the time series. 